Retrieve The Forgotten Memory
The problem Your AI agent is flying blind.
It greps through thousands of files, misses the doc that answers the question, invents modules that don't exist, forgets what you decided last session. The bigger the project, the worse it gets. You've added a smarter model. It didn't help. Because the bottleneck isn't intelligence — it's retrieval.
Code indexers (Augment, Sourcegraph, Cursor) only see code. But your project isn't just code. It's specs, PRs, architecture decisions, research papers, PDFs, regulations, vault notes — the context your agent needs to stop guessing.
Why I built this I was writing a French tax article (~50 pages of regulatory text, cross-references between code articles, case law, administrative doctrine). Claude Code kept grep-ing the same directories in loops, running out of context, and producing confidently wrong citations. I'd added more memory, better prompts, a smarter model. None of it worked, because the agent wasn't reasoning badly — it just couldn't find the right paragraph in a 2,000-file legal corpus. So I stopped trying to make the model smarter and built the layer it was missing. That's RTFM.
The solution RTFM indexes everything. One command, one SQLite file, one retrieval layer your agent queries before grepping.
pip install rtfm-ai && cd your-project && rtfm init 30 seconds. Claude Code now searches your indexed knowledge base — code and docs and PDFs and whatever else you drop in — with full-text, semantic, or hybrid search. The agent sees 300 tokens of metadata first, then expands only what's relevant. Progressive disclosure instead of context dumps.
Free. Runs locally. No API keys. No cloud. Your data stays yours.
Server Config
{
"mcpServers": {
"rtfm": {
"command": "rtfm-serve",
"args": [],
"env": {
"RTFM_DB": ".rtfm/library.db"
}
}
}
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